Deep learning molecular interaction motifs from receptor structures alone
Seeun Kim, Sung Won Oh, Hyeonuk Woo, Jiho Sim, Chaok Seok, Hahnbeom Park · Journal of Cheminformatics · 2025
Interactions of proteins with other molecules are often mediated by a set of critical binding motifs on their surfaces. Most traditional binder designs relied on motifs borrowed from known binder molecules, which highly restricted their applicability to novel targets or new binding sites. This work presents a deep learning network MotifGen that predicts potential binder motifs directly from receptor structures without further supporting information. MotifGen generates motif profiles at the receptor surface for 14 types of functional groups or 6 chemical interaction classes. These profiles are highly human-interpretable and can be further utilized as pre-trained embedding inputs for versatile few-shot binder design applications. We demonstrate MotifGen's effectiveness through its applications to peptide binder design and small molecule binding site prediction, where it either surpassed existing methods or added significant value when integrated. Our motif-centric approach can offer a new design strategy for novel binder discovery for challenging receptor targets. We introduce a new deep-learning based computational strategy for identifying potential binder motifs given a receptor structure. These predicted binder motifs can be directly applied to the design of various drugs types, including peptides and small molecules. To demonstrate its utility, we show its applications in peptide binder sequence discrimination and binding site prediction tasks, both of which are crucial tasks in structure-based drug design.